Missouri Botanical Garden Open Conference Systems, TDWG 2013 ANNUAL CONFERENCE

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#biobr – Citizen Science from social network
Etienne Americo Cartolano, Antonio Mauro Saraiva, Allan Koch Veiga

Building: Main Building 1st Floor
Room: Salone degli Oceani
Last modified: 2013-09-26

Abstract


The number and variety of citizen science projects has grown dramatically in recent years. These community-based projects promote the participation of amateurs to harness collective intelligence and to apply it to a scientific problem. This growth is accompanied by the uses of social networks to improve the number of contributors and the geographic extent of projects. In the case of the #biobr project, Twitter is being used to gather biodiversity data (image and text) published by spontaneous contributors, who use referenced words, such as scientific or popular names in their tweets. In addition, hashtags (#biobr, #pollinators, etc.) can be used to promote tweets and inspire new contributors in campaigns for biodiversity conservation. All geo-referenced tweets are plotted on a map available at www.biocomp.org.br/biobr.

However, the limited training, knowledge and expertise of contributors and their relative anonymity can lead to poor quality, with misleading or even malicious data being submitted. The absence of formal “scientific methods” and the use of non-standardized and poorly designed methods of data collection often lead to incomplete or inaccurate data. Also, the lack of commitment from volunteers in collecting field data can lead to gaps in the data across time and space. Subsequently, these issues have caused many in the scientific community to perceive citizen science data as low-quality and unworthy of being considered in serious scientific research.

Different proposed frameworks combine data quality control and trust/reputation metrics to provide an indication of the reliability of citizen science data. They provide mechanisms for improving and measuring the quality of citizen science data through both subjective and objective assessments of the data.

The project #biobr suggests the use of trust/reputation models to qualify the data gathered. In the current phase, the project has collected tweets and public information for the next analysis phase.